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<title>OpenCV: Face landmark detection in a video</title>
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<li class="navelem"><a class="el" href="../../d3/d81/tutorial_contrib_root.html">Tutorials for contrib modules</a></li><li class="navelem"><a class="el" href="../../de/d27/tutorial_table_of_content_face.html">Tutorials for face module</a></li>  </ul>
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<div class="title">Face landmark detection in a video </div>  </div>
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<div class="textblock"><p>This application lets you detect landmarks of detected faces in a video.This application first detects faces in a current video frame and then finds their facial landmarks. You just have to pass the video as input. </p><div class="fragment"><div class="line">// Command to be typed for running the sample</div><div class="line">./sampleDetectLandmarks -file=trained_model.dat -face_cascade=lbpcascadefrontalface.xml -video=/path_to_video/video.avi</div></div><!-- fragment --> <h2>Description of command parameters </h2>
<blockquote class="doxtable">
<ul>
<li><b>model_filename</b> f : (REQUIRED) A path to binary file storing the trained model which is to be loaded [example - /data/file.dat]</li>
<li><b>video</b> v : (REQUIRED) A path to video in which face landmarks have to be detected.[example - /data/video.avi]</li>
<li><b>face_cascade</b> c : (REQUIRED) A path to the face cascade xml file which you want to use as a face detector. </li>
</ul>
</blockquote>
<h3>Understanding code</h3>
<p>This tutorial will explain the sample code for face landmark detection. Jumping directly to the code :</p>
<div class="fragment"><div class="line">CascadeClassifier face_cascade;</div><div class="line">bool myDetector( InputArray image, OutputArray ROIs );</div><div class="line"></div><div class="line">bool myDetector( InputArray image, OutputArray ROIs ){</div><div class="line">    Mat gray;</div><div class="line">    std::vector&lt;Rect&gt; faces;</div><div class="line">    if(image.channels()&gt;1){</div><div class="line">        cvtColor(image.getMat(),gray,COLOR_BGR2GRAY);</div><div class="line">    }</div><div class="line">    else{</div><div class="line">        gray = image.getMat().clone();</div><div class="line">    }</div><div class="line">    equalizeHist( gray, gray );</div><div class="line">    face_cascade.detectMultiScale( gray, faces, 1.1, 3,0, Size(30, 30) );</div><div class="line">    Mat(faces).copyTo(ROIs);</div><div class="line">    return true;</div><div class="line">}</div></div><!-- fragment --><p> The facemark API provides the functionality to the user to use their own face detector to be used in face landmark detection.The above code creartes a sample face detector. The above function would be passed to a function pointer in the facemark API.</p>
<div class="fragment"><div class="line">VideoCapture cap(video);</div><div class="line">if(!cap.isOpened()){</div><div class="line">    cerr&lt;&lt;&quot;Video cannot be loaded. Give correct path&quot;&lt;&lt;endl;</div><div class="line">    return -1;</div><div class="line">}</div></div><!-- fragment --><p>The above code creates a video capture object and then loads the video. If the video is not loaded properly it prompts the user else the code proceeds.</p>
<div class="fragment"><div class="line">Mat img = imread(image);</div><div class="line">face_cascade.load(cascade_name);</div><div class="line">FacemarkKazemi::Params params;</div><div class="line">params.configfile = configfile_name;</div><div class="line">Ptr&lt;Facemark&gt; facemark = FacemarkKazemi::create(params);</div><div class="line">facemark-&gt;setFaceDetector(myDetector);</div></div><!-- fragment --><p> The above code creates a pointer of the face landmark detection class. The face detector created above has to be passed as function pointer to the facemark pointer created for detecting faces. </p><div class="fragment"><div class="line">vector&lt;Rect&gt; faces;</div><div class="line">vector&lt; vector&lt;Point2f&gt; &gt; shapes;</div><div class="line">Mat img;</div></div><!-- fragment --><p> The above code creates a vector to store the detected faces and a vector of vector to store shapes for each face detected in the current frame.</p>
<div class="fragment"><div class="line">while(1){</div><div class="line">    faces.clear();</div><div class="line">    shapes.clear();</div><div class="line">    cap&gt;&gt;img;</div><div class="line">    resize(img,img,Size(600,600),0,0,INTER_LINEAR_EXACT);</div><div class="line">    facemark-&gt;getFaces(img,faces);</div><div class="line">    if(faces.size()==0){</div><div class="line">        cout&lt;&lt;&quot;No faces found in this frame&quot;&lt;&lt;endl;</div><div class="line">    }</div><div class="line">    else{</div><div class="line">        for( size_t i = 0; i &lt; faces.size(); i++ )</div><div class="line">        {</div><div class="line">        cv::rectangle(img,faces[i],Scalar( 255, 0, 0 ));</div><div class="line">        }</div><div class="line">        if(facemark-&gt;fit(img,faces,shapes))</div><div class="line">        {</div><div class="line">        for(unsigned long i=0;i&lt;faces.size();i++){</div><div class="line">            for(unsigned long k=0;k&lt;shapes[i].size();k++)</div><div class="line">                cv::circle(img,shapes[i][k],3,cv::Scalar(0,0,255),FILLED);</div><div class="line">        }</div><div class="line">        }</div><div class="line">    }</div><div class="line">    namedWindow(&quot;Detected_shape&quot;);</div><div class="line">    imshow(&quot;Detected_shape&quot;,img);</div><div class="line">    if(waitKey(1) &gt;= 0) break;</div><div class="line">}</div></div><!-- fragment --><p>The above code then reads each frame and detects faces and the landmarks corresponding to each shape detected. It then displays the current frame.</p>
<p>After running the above code you will get results something like this</p>
<p>Sample video:</p>
<p> 
<iframe width="560" height="315" src="https://www.youtube.com/embed/ZtaV07T90D8" frameborder="0" allowfullscreen></iframe>
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